Overview

Dataset statistics

Number of variables10
Number of observations6329
Missing cells6937
Missing cells (%)11.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory201.1 B

Variable types

Text2
Numeric7
Unsupported1

Alerts

avg_age is highly overall correlated with avg_age_men and 1 other fieldsHigh correlation
avg_age_men is highly overall correlated with avg_age and 1 other fieldsHigh correlation
avg_age_women is highly overall correlated with avg_age and 1 other fieldsHigh correlation
men is highly overall correlated with population and 1 other fieldsHigh correlation
population is highly overall correlated with men and 1 other fieldsHigh correlation
women is highly overall correlated with men and 1 other fieldsHigh correlation
municipality_code has 76 (1.2%) missing valuesMissing
municipality_name has 76 (1.2%) missing valuesMissing
population has 76 (1.2%) missing valuesMissing
men has 76 (1.2%) missing valuesMissing
women has 76 (1.2%) missing valuesMissing
avg_age has 76 (1.2%) missing valuesMissing
avg_age_men has 76 (1.2%) missing valuesMissing
avg_age_women has 76 (1.2%) missing valuesMissing
Unnamed: 9 has 6329 (100.0%) missing valuesMissing
population is highly skewed (γ1 = 61.21251491)Skewed
men is highly skewed (γ1 = 61.32708184)Skewed
women is highly skewed (γ1 = 61.10206477)Skewed
Unnamed: 9 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2026-01-03 09:59:11.564478
Analysis finished2026-01-03 09:59:22.154078
Duration10.59 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Distinct153
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size342.8 KiB
2026-01-03T10:59:22.342958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length6
Mean length6.1063359
Min length6

Characters and Unicode

Total characters38647
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)1.2%

Sample

1st rowCZ0100
2nd rowOkres Benešov
3rd rowCZ0201
4th rowCZ0201
5th rowCZ0201
ValueCountFrequency (%)
cz0643187
 
2.9%
cz0635174
 
2.7%
cz0634167
 
2.6%
cz0647144
 
2.2%
cz0632123
 
1.9%
cz0633120
 
1.9%
cz0631120
 
1.9%
cz0207120
 
1.9%
cz020b119
 
1.9%
cz0641116
 
1.8%
Other values (158)5036
78.4%
2026-01-03T10:59:22.597520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
07697
19.9%
C6339
16.4%
Z6255
16.2%
23705
9.6%
33376
8.7%
12440
 
6.3%
42232
 
5.8%
51875
 
4.9%
61753
 
4.5%
71157
 
3.0%
Other values (58)1818
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)38647
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07697
19.9%
C6339
16.4%
Z6255
16.2%
23705
9.6%
33376
8.7%
12440
 
6.3%
42232
 
5.8%
51875
 
4.9%
61753
 
4.5%
71157
 
3.0%
Other values (58)1818
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)38647
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07697
19.9%
C6339
16.4%
Z6255
16.2%
23705
9.6%
33376
8.7%
12440
 
6.3%
42232
 
5.8%
51875
 
4.9%
61753
 
4.5%
71157
 
3.0%
Other values (58)1818
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)38647
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07697
19.9%
C6339
16.4%
Z6255
16.2%
23705
9.6%
33376
8.7%
12440
 
6.3%
42232
 
5.8%
51875
 
4.9%
61753
 
4.5%
71157
 
3.0%
Other values (58)1818
 
4.7%

municipality_code
Real number (ℝ)

Missing 

Distinct6253
Distinct (%)100.0%
Missing76
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean561865.08
Minimum500011
Maximum599999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.6 KiB
2026-01-03T10:59:22.704092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum500011
5-th percentile529640.8
Q1542598
median563536
Q3581445
95-th percentile596122.4
Maximum599999
Range99988
Interquartile range (IQR)38847

Descriptive statistics

Standard deviation23218.218
Coefficient of variation (CV)0.041323476
Kurtosis-0.67627247
Mean561865.08
Median Absolute Deviation (MAD)18808
Skewness-0.29211376
Sum3.5133423 × 109
Variance5.3908566 × 108
MonotonicityNot monotonic
2026-01-03T10:59:22.807426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5111611
 
< 0.1%
5110211
 
< 0.1%
5295411
 
< 0.1%
5684491
 
< 0.1%
5547821
 
< 0.1%
5293031
 
< 0.1%
5325681
 
< 0.1%
5307431
 
< 0.1%
5323801
 
< 0.1%
5320961
 
< 0.1%
Other values (6243)6243
98.6%
(Missing)76
 
1.2%
ValueCountFrequency (%)
5000111
< 0.1%
5000201
< 0.1%
5000461
< 0.1%
5000621
< 0.1%
5000711
< 0.1%
5001011
< 0.1%
5001271
< 0.1%
5001351
< 0.1%
5001511
< 0.1%
5001601
< 0.1%
ValueCountFrequency (%)
5999991
< 0.1%
5999641
< 0.1%
5999561
< 0.1%
5999481
< 0.1%
5999301
< 0.1%
5999211
< 0.1%
5999051
< 0.1%
5998671
< 0.1%
5998321
< 0.1%
5998081
< 0.1%

municipality_name
Text

Missing 

Distinct5345
Distinct (%)85.5%
Missing76
Missing (%)1.2%
Memory size504.6 KiB
2026-01-03T10:59:22.987187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length33
Median length30
Mean length8.7804254
Min length2

Characters and Unicode

Total characters54904
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4754 ?
Unique (%)76.0%

Sample

1st rowPraha
2nd rowBenešov
3rd rowBernartice
4th rowBílkovice
5th rowBlažejovice
ValueCountFrequency (%)
nad195
 
2.5%
u118
 
1.5%
horní89
 
1.1%
dolní77
 
1.0%
lhota64
 
0.8%
ves49
 
0.6%
pod48
 
0.6%
nová47
 
0.6%
újezd44
 
0.6%
velké26
 
0.3%
Other values (4941)7106
90.4%
2026-01-03T10:59:23.248923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o4895
 
8.9%
e4793
 
8.7%
i3040
 
5.5%
a2997
 
5.5%
v2934
 
5.3%
n2799
 
5.1%
c2659
 
4.8%
l2008
 
3.7%
r1943
 
3.5%
t1687
 
3.1%
Other values (56)25149
45.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)54904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o4895
 
8.9%
e4793
 
8.7%
i3040
 
5.5%
a2997
 
5.5%
v2934
 
5.3%
n2799
 
5.1%
c2659
 
4.8%
l2008
 
3.7%
r1943
 
3.5%
t1687
 
3.1%
Other values (56)25149
45.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)54904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o4895
 
8.9%
e4793
 
8.7%
i3040
 
5.5%
a2997
 
5.5%
v2934
 
5.3%
n2799
 
5.1%
c2659
 
4.8%
l2008
 
3.7%
r1943
 
3.5%
t1687
 
3.1%
Other values (56)25149
45.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)54904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o4895
 
8.9%
e4793
 
8.7%
i3040
 
5.5%
a2997
 
5.5%
v2934
 
5.3%
n2799
 
5.1%
c2659
 
4.8%
l2008
 
3.7%
r1943
 
3.5%
t1687
 
3.1%
Other values (56)25149
45.8%

population
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct2141
Distinct (%)34.2%
Missing76
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean1733.762
Minimum0
Maximum1397880
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size49.6 KiB
2026-01-03T10:59:23.335085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile90
Q1223
median452
Q3987
95-th percentile4475.6
Maximum1397880
Range1397880
Interquartile range (IQR)764

Descriptive statistics

Standard deviation19468.859
Coefficient of variation (CV)11.229257
Kurtosis4268.9918
Mean1733.762
Median Absolute Deviation (MAD)285
Skewness61.212515
Sum10841214
Variance3.7903646 × 108
MonotonicityNot monotonic
2026-01-03T10:59:23.422799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12320
 
0.3%
16218
 
0.3%
11018
 
0.3%
18316
 
0.3%
17016
 
0.3%
37415
 
0.2%
14215
 
0.2%
21314
 
0.2%
22114
 
0.2%
13414
 
0.2%
Other values (2131)6093
96.3%
(Missing)76
 
1.2%
ValueCountFrequency (%)
04
0.1%
161
 
< 0.1%
191
 
< 0.1%
251
 
< 0.1%
282
< 0.1%
311
 
< 0.1%
331
 
< 0.1%
352
< 0.1%
361
 
< 0.1%
374
0.1%
ValueCountFrequency (%)
13978801
< 0.1%
4027391
< 0.1%
2831871
< 0.1%
1879281
< 0.1%
1080901
< 0.1%
1030631
< 0.1%
972311
< 0.1%
943111
< 0.1%
923191
< 0.1%
908661
< 0.1%

men
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct1511
Distinct (%)24.2%
Missing76
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean851.05757
Minimum0
Maximum679162
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size49.6 KiB
2026-01-03T10:59:23.510541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile45.6
Q1114
median231
Q3496
95-th percentile2216.4
Maximum679162
Range679162
Interquartile range (IQR)382

Descriptive statistics

Standard deviation9452.3567
Coefficient of variation (CV)11.106601
Kurtosis4280.6051
Mean851.05757
Median Absolute Deviation (MAD)145
Skewness61.327082
Sum5321663
Variance89347047
MonotonicityNot monotonic
2026-01-03T10:59:23.601136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9127
 
0.4%
8226
 
0.4%
6926
 
0.4%
10225
 
0.4%
6325
 
0.4%
11124
 
0.4%
12624
 
0.4%
11224
 
0.4%
12123
 
0.4%
7123
 
0.4%
Other values (1501)6006
94.9%
(Missing)76
 
1.2%
ValueCountFrequency (%)
04
0.1%
82
 
< 0.1%
152
 
< 0.1%
161
 
< 0.1%
173
 
< 0.1%
184
0.1%
195
0.1%
203
 
< 0.1%
217
0.1%
229
0.1%
ValueCountFrequency (%)
6791621
< 0.1%
1956151
< 0.1%
1373971
< 0.1%
908481
< 0.1%
519891
< 0.1%
488011
< 0.1%
465061
< 0.1%
451241
< 0.1%
447061
< 0.1%
441711
< 0.1%

women
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct1537
Distinct (%)24.6%
Missing76
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean882.70446
Minimum0
Maximum718718
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size49.6 KiB
2026-01-03T10:59:23.692967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile44
Q1109
median224
Q3490
95-th percentile2271
Maximum718718
Range718718
Interquartile range (IQR)381

Descriptive statistics

Standard deviation10016.651
Coefficient of variation (CV)11.347684
Kurtosis4257.8036
Mean882.70446
Median Absolute Deviation (MAD)143
Skewness61.102065
Sum5519551
Variance1.0033329 × 108
MonotonicityNot monotonic
2026-01-03T10:59:23.776512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8130
 
0.5%
4529
 
0.5%
11129
 
0.5%
5027
 
0.4%
5726
 
0.4%
7025
 
0.4%
5825
 
0.4%
8225
 
0.4%
7524
 
0.4%
6124
 
0.4%
Other values (1527)5989
94.6%
(Missing)76
 
1.2%
ValueCountFrequency (%)
04
0.1%
81
 
< 0.1%
91
 
< 0.1%
111
 
< 0.1%
121
 
< 0.1%
133
< 0.1%
141
 
< 0.1%
151
 
< 0.1%
161
 
< 0.1%
173
< 0.1%
ValueCountFrequency (%)
7187181
< 0.1%
2071241
< 0.1%
1457901
< 0.1%
970801
< 0.1%
561011
< 0.1%
542621
< 0.1%
507251
< 0.1%
491871
< 0.1%
476131
< 0.1%
466951
< 0.1%

avg_age
Real number (ℝ)

High correlation  Missing 

Distinct200
Distinct (%)3.2%
Missing76
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean42.948761
Minimum0
Maximum64.3
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size49.6 KiB
2026-01-03T10:59:23.872225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38.8
Q141.4
median42.9
Q344.4
95-th percentile47.3
Maximum64.3
Range64.3
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8651128
Coefficient of variation (CV)0.066710022
Kurtosis34.465921
Mean42.948761
Median Absolute Deviation (MAD)1.5
Skewness-1.6290455
Sum268558.6
Variance8.2088712
MonotonicityNot monotonic
2026-01-03T10:59:23.961743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.3138
 
2.2%
42.6130
 
2.1%
43119
 
1.9%
41.8115
 
1.8%
42.7115
 
1.8%
42.9114
 
1.8%
43.6112
 
1.8%
43.4110
 
1.7%
42.1109
 
1.7%
43.8109
 
1.7%
Other values (190)5082
80.3%
ValueCountFrequency (%)
04
0.1%
31.31
 
< 0.1%
32.21
 
< 0.1%
331
 
< 0.1%
33.21
 
< 0.1%
33.31
 
< 0.1%
33.62
< 0.1%
33.81
 
< 0.1%
33.92
< 0.1%
34.31
 
< 0.1%
ValueCountFrequency (%)
64.31
< 0.1%
64.11
< 0.1%
61.71
< 0.1%
59.32
< 0.1%
58.51
< 0.1%
58.11
< 0.1%
57.22
< 0.1%
56.81
< 0.1%
56.31
< 0.1%
561
< 0.1%

avg_age_men
Real number (ℝ)

High correlation  Missing 

Distinct221
Distinct (%)3.5%
Missing76
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean42.049368
Minimum0
Maximum63.5
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size49.6 KiB
2026-01-03T10:59:24.048950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile37.9
Q140.5
median42
Q343.5
95-th percentile46.6
Maximum63.5
Range63.5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.9599732
Coefficient of variation (CV)0.070392809
Kurtosis27.724203
Mean42.049368
Median Absolute Deviation (MAD)1.5
Skewness-1.2968291
Sum262934.7
Variance8.7614411
MonotonicityNot monotonic
2026-01-03T10:59:24.281894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.2135
 
2.1%
41.9133
 
2.1%
42.3130
 
2.1%
41.4124
 
2.0%
42118
 
1.9%
41.2117
 
1.8%
41.8116
 
1.8%
41.3114
 
1.8%
42.4112
 
1.8%
42.5109
 
1.7%
Other values (211)5045
79.7%
ValueCountFrequency (%)
04
0.1%
30.71
 
< 0.1%
31.11
 
< 0.1%
31.21
 
< 0.1%
31.81
 
< 0.1%
321
 
< 0.1%
32.11
 
< 0.1%
32.21
 
< 0.1%
32.41
 
< 0.1%
32.71
 
< 0.1%
ValueCountFrequency (%)
63.51
< 0.1%
58.61
< 0.1%
57.62
< 0.1%
57.41
< 0.1%
56.71
< 0.1%
56.41
< 0.1%
561
< 0.1%
55.51
< 0.1%
55.41
< 0.1%
54.81
< 0.1%

avg_age_women
Real number (ℝ)

High correlation  Missing 

Distinct232
Distinct (%)3.7%
Missing76
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean43.8913
Minimum0
Maximum68.2
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size49.6 KiB
2026-01-03T10:59:24.394045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile39.2
Q142
median43.8
Q345.7
95-th percentile48.7
Maximum68.2
Range68.2
Interquartile range (IQR)3.7

Descriptive statistics

Standard deviation3.3104366
Coefficient of variation (CV)0.075423525
Kurtosis22.299383
Mean43.8913
Median Absolute Deviation (MAD)1.8
Skewness-0.90152471
Sum274452.3
Variance10.95899
MonotonicityNot monotonic
2026-01-03T10:59:24.505222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.9109
 
1.7%
43.1106
 
1.7%
43.4106
 
1.7%
44103
 
1.6%
43.6102
 
1.6%
44.297
 
1.5%
44.695
 
1.5%
43.393
 
1.5%
43.792
 
1.5%
44.492
 
1.5%
Other values (222)5258
83.1%
ValueCountFrequency (%)
04
0.1%
31.61
 
< 0.1%
31.81
 
< 0.1%
32.71
 
< 0.1%
33.11
 
< 0.1%
33.42
< 0.1%
33.52
< 0.1%
33.72
< 0.1%
33.92
< 0.1%
34.12
< 0.1%
ValueCountFrequency (%)
68.21
< 0.1%
681
< 0.1%
66.21
< 0.1%
65.11
< 0.1%
64.81
< 0.1%
62.91
< 0.1%
611
< 0.1%
601
< 0.1%
59.71
< 0.1%
59.51
< 0.1%

Unnamed: 9
Unsupported

Missing  Rejected  Unsupported 

Missing6329
Missing (%)100.0%
Memory size49.6 KiB

Interactions

2026-01-03T10:59:20.164921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:12.493665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:13.731395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:14.974494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:16.179828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:17.398675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:18.661173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:20.472082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:12.696671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:13.974098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:15.130367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:16.373666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:17.550935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:18.822644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:20.713406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:12.860449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:14.138831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:15.312294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:16.544162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:17.732355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:19.122365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:20.955194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:13.020851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:14.307749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:15.458601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:16.716472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:17.891290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:19.490240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:21.131626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:13.224357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:14.475980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:15.678959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:16.894274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:18.045603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:19.679598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:21.278740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:13.388816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:14.655130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:15.861566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:17.055695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:18.215441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:19.830609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:21.510720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:13.567213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:14.817172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:16.018301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:17.239555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:18.407316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T10:59:19.980915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-03T10:59:24.603802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
avg_ageavg_age_menavg_age_womenmenmunicipality_codepopulationwomen
avg_age1.0000.8810.896-0.1100.031-0.111-0.112
avg_age_men0.8811.0000.608-0.1870.016-0.185-0.182
avg_age_women0.8960.6081.000-0.0340.051-0.039-0.044
men-0.110-0.187-0.0341.000-0.0230.9990.996
municipality_code0.0310.0160.051-0.0231.000-0.024-0.025
population-0.111-0.185-0.0390.999-0.0241.0000.999
women-0.112-0.182-0.0440.996-0.0250.9991.000

Missing values

2026-01-03T10:59:21.767628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-03T10:59:21.867484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-01-03T10:59:22.062709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

district_codemunicipality_codemunicipality_namepopulationmenwomenavg_ageavg_age_menavg_age_womenUnnamed: 9
0CZ0100554782.0Praha1397880.0679162.0718718.041.940.443.3NaN
1Okres BenešovNaNNaNNaNNaNNaNNaNNaNNaNNaN
2CZ0201529303.0Benešov17043.08058.08985.043.942.245.4NaN
3CZ0201532568.0Bernartice223.0102.0121.045.246.044.5NaN
4CZ0201530743.0Bílkovice225.0113.0112.045.845.646.0NaN
5CZ0201532380.0Blažejovice102.050.052.049.847.851.8NaN
6CZ0201532096.0Borovnice91.042.049.041.238.343.7NaN
7CZ0201532924.0Bukovany824.0429.0395.041.040.741.3NaN
8CZ0201529451.0Bystřice4698.02361.02337.042.141.143.0NaN
9CZ0201532690.0Ctiboř179.086.093.035.837.734.1NaN
district_codemunicipality_codemunicipality_namepopulationmenwomenavg_ageavg_age_menavg_age_womenUnnamed: 9
6319CZ0806599549.0Klimkovice4454.02183.02271.043.141.844.4NaN
6320CZ0806554049.0Olbramice735.0377.0358.042.040.543.5NaN
6321CZ0806554821.0Ostrava283187.0137397.0145790.043.942.045.7NaN
6322CZ0806598739.0Stará Ves nad Ondřejnicí2992.01497.01495.042.441.143.7NaN
6323CZ0806598798.0Šenov6639.03275.03364.043.442.444.5NaN
6324CZ0806598836.0Václavovice2136.01058.01078.042.441.643.1NaN
6325CZ0806510882.0Velká Polom2194.01075.01119.041.440.941.9NaN
6326CZ0806598879.0Vratimov7434.03654.03780.042.841.444.1NaN
6327CZ0806500291.0Vřesina2820.01410.01410.044.643.545.8NaN
6328CZ0806568449.0Zbyslavice664.0337.0327.042.541.243.8NaN